Image processing method and system for iris recognition

ABSTRACT

An image processing method for iris recognition of a predetermined subject, comprises acquiring through an image sensor, a probe image illuminated by an infra-red (IR) illumination source, wherein the probe image comprises one or more eye regions and is overexposed until skin portions of the image are saturated. One or more iris regions are identified within the one or more eye regions of said probe image; and the identified iris regions are analysed to detect whether they belong to the predetermined subject.

FIELD

The present invention relates to an image processing method and systemfor iris recognition.

BACKGROUND

The iris surrounds the dark, inner pupil region of an eye and extendsconcentrically to the white sclera of the eye.

A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometricrecognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, 2004discloses that the iris of the eye is a near-ideal biometric.

For the purposes of recognition, an image of an iris region is typicallyacquired in an imaging system that uses infra-red (IR) illumination tobring out the main features of an underlying iris pattern. An irispattern is a gray-scale/luminance pattern evident within an iris regionthat can be processed to yield an iris code. The iris pattern can bedefined in terms of polar co-ordinates and these are typically convertedinto rectangular coordinates prior to analysis to extract the underlyingiris code.

Strong sunlight can affect the performance of iris recognition systemsbecause of shadows on a subject's face and/or light patterns beingreflected from the iris, causing a significant performance degradation.

Further, it is known that substitute images containing iris regions of aperson to be recognized, such as images shown in pictures, artificialobjects, or monitors, can be presented to an iris recognition system,instead of the person themselves, for fraudulent purposes—this iscommonly referred to as spoofing.

SUMMARY

According to a first aspect of the present invention there is providedan image processing method for iris recognition according to claim 1.

According to a second aspect there is provided an image processingmethod for liveness iris recognition according to claim 17.

There is also provided an image processing system for iris recognitionaccording to claim 18 and a computer program product according to claim19.

Embodiments are based on the observation that IR light is reflected in adifferent way from the human eye than from the skin of the face of aliving subject. The difference in reflection is such that eye regionswithin an image are still detectable when the image skin portionssaturate.

The fact that iris regions can be detected in an image overexposed sothat skin portions are saturated indicates the liveness of a subjectunder recognition, and hence the method can proceed with theauthentication of the live subject. In this way, embodiments are notonly improved in view of possible strong sunlight conditions, but alsoprovide robust liveness detection.

In particular, as long as exposure time is not too long (and this isusually possible in strong sunlight conditions) iris details can be morereadily detected within an overexposed image compared to performingimage acquisition with a normally exposed image where it is notdesirable to saturate large portions of an image or indeed to expose theimage for long enough to do so.

Hence, acquiring an iris region during an enrolment stage from anoverexposed reference image and using it in the following authenticationstage improves the accuracy and reliability of the whole irisrecognition process, especially in case of strong sunlight.

According to one embodiment, authentication can switch between twoworking modes: a normal light mode for standard operation with normallight conditions, and a strong light mode for working with imagesaffected by strong ambient light conditions.

Accordingly, the enrolment stage of the iris recognition may compriseacquiring a normally-exposed reference image and an overexposedreference image, and generating and storing information from the irisregions of these reference images.

During the authentication stage of the process, if ambient light doesnot exceed a threshold, the process can run in a substantially standardmode, where the iris regions of a normally-exposed probe image can becompared for matching purposes with the stored information for the irisregions from the normally-exposed reference image.

Otherwise, the process can switch to a strong light mode, where the irisregions of an overexposed probe image recognition can be compared formatching purposes with the stored information for the iris regions fromthe overexposed reference image.

Acquiring an iris region during the enrolment stage from an overexposedreference image and using it in the following authentication stagefurther improve the accuracy and reliability of the iris recognitionprocess working in the strong light mode.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of example,with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an exemplary image processing systemaccording to an embodiment of the present invention;

FIGS. 2(a) and 2(b) illustrate normally-exposed NIR face images affectedby strong sunlight;

FIG. 3 illustrates an overexposed version of the NIR face image of FIG.2(b);

FIGS. 4 and 5 illustrate an iris image acquired with a normal exposureand an overexposure, respectively; and

FIGS. 6 and 7 illustrate an enrolment stage and an authentication stage,respectively, of an exemplary method.

DETAILED DESCRIPTION

Referring now to FIG. 1 there is shown an image processing system 10 forperforming iris recognition according to an embodiment of the presentinvention.

The system 10, which may comprise for example, a camera, a smartphone, atablet or the like, comprises a central processing unit (CPU) 24 whichtypically runs operating system software as well as general purposeapplication software, for example, camera applications, browser,messaging, e-mail or other apps. The operating system may be set so thata user must authenticate themselves to unlock the system and to gainaccess to applications installed on the system; or individualapplications running on the system may require a user to authenticatethemselves before they gain access to sensitive information.

The system 10 comprises a lens assembly 12, an image sensor 14, and atleast one IR illumination source 16 capable of acquiring an image, suchas a facial image of a predetermined subject to be recognized andauthenticated by the system 10.

The IR illumination source 16, which can for example be a NIR LED, isconfigured to illuminate a subject with IR light, preferably NIR light(that is light of approximately 700-1000 nm in wavelength). One suitableLED comprises an 810 nm SFH 4780S LED from OSRAM. In some embodimentsmore than one illumination source or a tunable illumination source maybe employed to emit IR light at different wavelengths.

The lens 12 is configured for focusing IR light reflected from anilluminated subject onto the sensor 14.

A first exemplary lens assembly 12 is disclosed in WO/2016/020147 (Ref:FN-397), the disclosure of which is incorporated herein by reference,which comprises a plurality of lens elements arranged to simultaneouslyfocus NIR light received from a given object through central andperipheral apertures of a filter, and visible light received from theobject through the central aperture onto the sensor surface.

A second exemplary lens assembly 12 is disclosed in PCT/EP2016/052395(Ref: FN-452), the disclosure of which is incorporated herein byreference, which comprises a collecting lens surface with an opticalaxis and which is arranged to focus IR light received from a givenobject distance on the image sensor surface. The lens assembly includesat least a first reflective surface for reflecting collected light alongan axis transverse to the optical axis, so that a length of the opticalsystem along the optical axis is reduced by comparison to a focal lengthof the lens assembly.

A third exemplary lens assembly 12 is disclosed in PCT/EP2016/060941(Ref: FN-466), the disclosure of which is incorporated herein byreference, which comprises a cluster of at least two lenses arranged infront of the image sensor with each lens' optical axis in parallelspaced apart relationship. Each lens has a fixed focus and a differentaperture to provide a respective angular field of view. The lens withthe closest focus has the smallest aperture and the lens with thefarthest focus has the largest aperture, so that iris images can beacquired from subjects at distances from between about 200 mm to 500 mmfrom an acquisition device.

A fourth exemplary lens assembly 12 is disclosed in U.S. patentapplication Ser. No. 15/186,283 filed 17 Jun. 2016 (Ref: FN-477), thedisclosure of which is incorporated herein by reference, which comprisesan image sensor comprising an array of pixels including pixels sensitiveto NIR wavelengths; at least one NIR light source capable of selectivelyemitting light with different discrete NIR wavelengths; and a processor,operably connected to the image sensor and the at least one NIR lightsource, to acquire image information from the sensor under illuminationat one of the different discrete NIR wavelengths. A lens assemblycomprises a plurality of lens elements with a total track length no morethan 4.7 mm, each lens element comprising a material with a refractiveindex inversely proportional to wavelength. The different discrete NIRwavelengths are matched with the refractive index of the material forthe lens elements to balance axial image shift induced by a change inobject distance with axial image shift due to change in illuminationwavelength.

Other variants of these lens assemblies are of course possible.

Preferably, the sensor 14 comprises an array of pixels including pixelssensitive to IR wavelengths. For example, the sensor 14 may compriseeither a dedicated IR image sensor, a conventional type RGB type array,where the natural IR sensitivity of the RGB pixels is used to acquireboth visible wavelength images as well as images illuminated with the IRillumination source 16. Alternatively, the sensor 14 may comprise anRGBW (where the white pixels are also IR sensitive) or RGB-IR array(including IR only sensitive pixels) where visible and IR images of ascene can be acquired substantially simultaneously.

Typically, images acquired from the image sensor 14 are written intomemory 22 as required either by applications being executed by the CPU24 or other dedicated processing blocks which have access to the imagesensor 14 and/or memory 22 across the system bus 26.

In the embodiment, the system 10 further comprises a dedicatedface/eye/iris detector 18 for identifying a face region within anacquired image, and within a given face region, one or more eye regionsand iris regions within those eye regions. This functionality couldequally be implemented in software executed by the CPU 24.

Face detection in real-time has become a standard feature of mostdigital imaging devices and there are many techniques for identifyingsuch regions within an acquired image, for example, as disclosed inWO2008/018887 (Reference: FN-143), the disclosure of which isincorporated herein by reference. Further, most cameras and smartphonesalso support the real-time detection of various facial features and canidentify specific patterns such as ‘eye-blink’ and ‘smile’ so that forexample, the timing of main image acquisition can be adjusted to ensuresubjects within a scene are in-focus, not blinking or are smiling suchas disclosed in WO2007/106117 (Reference: FN-149), the disclosure ofwhich is incorporated herein by reference. Where such functionality isavailable in an image processing device, detecting and tracking faceregions and eye regions within those face regions imposes no additionaloverhead and so this information is available continuously for a streamof images being acquired by the system 10.

The iris regions are extracted from the identified eye regions and amore detailed analysis may be performed to confirm if a valid irispattern is detectable. J. Daugman, “New methods in iris recognition,”IEEE Trans. Syst. Man. Cybern. B. Cybern., vol. 37, pp. 1167-1175, 2007discloses a range of additional refinements which can be utilized todetermine the exact shape of iris and the eye-pupil. It is also commonpractice to transform the iris from a polar to rectangular co-ordinatesystem, although this is not necessary.

Hence, the output of the detector 18 comprises one or more iris regionsidentified within an acquired image.

The data for the identified one or more iris regions may be stored inthe system memory 22 and/or other memories such as secure memory ordatabases belonging to or separate from the system 10.

Iris regions identified within an acquired image can be used as an inputfor a biometric authentication unit (BAU) 20. The BAU 20 is configuredfor analyzing the received iris regions in order to detect whether theybelong to a predetermined subject.

Preferably, the BAU 20 is configured for extracting an iris code fromthe received iris regions, and it may store this code in the memory 22and/or other memories or databases belonging to or separate from thesystem 10.

Further, the BAU 20 is preferably configured to compare the received oneor more iris regions with reference iris region(s) associated to thepredetermined subject, which can be stored in memory 22, within securememory in the BAU 20 or in any location accessible to the BAU 20.

An exemplary way for performing iris code extraction and comparisonbetween iris regions is disclosed in WO2011/124512 (Reference: FN-458),the disclosure of which is incorporated herein by reference, and thisinvolves a comparison between two image templates using a master mask toselect corresponding codes from the templates. The master mask excludesblocks from the matching process and/or weights blocks according totheir known or expected reliability.

Referring now to FIGS. 6 and 7, the system of the embodiment operates intwo-stages, an enrollment stage illustrated in FIG. 6 and anauthentication stage illustrated in FIG. 7. Each stage can beimplemented with either operating system software or applicationsoftware or within a dedicated hardware module such as a modified BAU20. Where the stage is driven by software running on the CPU, thesoftware can utilize the functionality of dedicated processing modulessuch as the BAU 20 or detector 18 through their respective APIs.

In any case, the enrollment stage begins when a user indicates that theywish to either secure the entire system or a given application runningon the system based on iris recognition.

At step 99 enrollment begins with the acquisition of a reference imageat a normal exposure time T_(RN) (an exemplary eye region 56 from anormally-exposed reference image is illustrated in FIG. 4). Typically,T_(RN) is sufficiently long to ensure that only a minimum number ofpixels within the acquired image are saturated to ensure suitablecontrast within the image. Clearly, step 99 may involve the acquisitionof as many images as are required until a face comprising an eye regionis imaged at a suitable distance from the camera so that the subject isin focus and where subject motion and camera motion are limited so thatthe eye region contains an iris region 52 in sufficiently clear detailto enable an iris code to be extracted for the subject.

It will therefore be seen that is desirable to acquire the referenceimage with as short an exposure time T_(RN) as possible to ensure theacquired reference image is sharp.

In order to test whether the acquired reference image is suitable, theenrolment stage can further comprise extracting at least one iris region52 from the normally-exposed reference image 56, for example using themodule 18, step 105, and generating and storing iris information for theextracted iris region(s), using the BAU 20, step 106. If an iris code issuccessfully extracted and stored, this process can stop, otherwise, afurther candidate reference image can be acquired to attempt to extractand store iris information from a reference image.

The process continues by acquiring a second reference image where skinportions of the imaged face are saturated, step 100. In the embodiment,step 100 comprises:

-   -   deriving a skin map for the reference image, step 100a. There        are many techniques for identifying regions of a facial image        comprising skin—as compared to eyes, hair, mouth or background.        Some of these can comprise performing pattern recognition on an        intensity image such as shown in FIG. 4, whereas others might        operate on a color version of the IR intensity image where this        is available—for example, where an RGB-IR or RGBW image sensor        14 is employed. An example of deriving a skin map of the image        under acquisition is disclosed in US2003/0179911, the disclosure        of which is incorporated herein by reference, where an image is        segmented into regions each of which has a substantially        homogeneous color; in particular, regions having a predominantly        skin color are mapped;    -   based on the derived skin map, an exposure time T_(RO) greater        than T_(RN) and sufficient to ensure that regions of skin within        an image captured for such an exposure time are saturated is        derived, step 100 b. There are many possible approaches to        determining T_(RO), for example:        T _(RO) =T _(RN) *K        wherein K is the ratio of the pixels saturation level value (255        for an 8-bit pixel) to the lowest intensity value (I_(L)) among        the detected skin pixels. On the other hand, in order to prevent        outliers unduly affecting the calculation, I_(L) can be chosen        as the maximum intensity level for the n % least bright skin        pixels, where n is between 5-25%. In other variations I_(L) may        be chosen as a function of the average or modal intensities of        skin pixels within the reference image, and indeed in further        variants T_(RO) may be determined independently of T_(RN).        Nonetheless, while T_(RO) should be longer than T_(RN) it is        important that it is not chosen to be so long that an image        captured for that exposure time would be subject to undue camera        or subject motion blur. For this reason, enrollment should        preferably be performed in a well illuminated environment and/or        the illumination source 16 should be sufficient to ensure T_(RO)        does not need to be excessive; and    -   acquiring an overexposed reference image during T_(RO), step        100 c. An exemplary eye region 46 for such an overexposed image        is shown in FIG. 5. Here it will be noted that the skin regions        41 surrounding the eye are largely saturated (white), but also        that the details of the iris region 42 acquired from the        overexposed image 46 are more visible and readily detectable        compared to a normally exposed image such as shown in FIG. 4. As        indicated in FIG. 6, if for either reasons of not choosing        T_(RO) sufficiently well, either too-long and so causing blur or        too short and not sufficiently saturating skin pixels, possibly        due to a change in subject conditions, steps 101 a-c can be        repeated with different exposure times.

The process then continues with the step 101 of detecting whether theeyes regions within the overexposed image 46 acquired at step 100 can bedetected.

If so, it indicates that the acquired eye regions belong to a livesubject and the enrolment stage can proceed with the execution of steps102 and 103.

If not, the most likely reason is that a user has presented a facsimilefacial image to the camera and as well as the skin regions of the image,the eye regions have also saturated.

It will be appreciated that IR light, especially NIR, is reflected in adifferent way from the human eye and from the skin of the face of aliving person.

The difference in reflection is such that the image acquired while beingilluminated by the IR light (preferably, by the NIR light source 16) canbe overexposed until saturation of its skin portions, while its eyesregions do not saturate.

For example, FIG. 3 illustrates an acquired NIR image 36 overexposeduntil its skin portions 31 are saturated, where it is evident how thenot-saturated eye regions 30 remain viewable and detectable in contrastto the surrounding saturated skin portions 31.

Comparing FIG. 3 to FIG. 2(b) (illustrating an NIR image acquired withnormal exposure and affected by strong sunlight), it is also evident howthe iris regions 32 illustrated in FIG. 3 are more readily identifiableand detectable compared to iris regions 32 illustrated in FIG. 2(b).This because the saturation of the skin portions 31 causes a reductionof the sunlight effects, such as the face shadows 33 viewable in FIGS.2(a) and (b), which affect the eye regions 30.

In this way, the acquisition of the overexposed image 36 improves theiris recognition of the identified iris regions 32, especially in caseof strong sunlight.

On the other hand, eye regions from facsimile facial images will tend tosaturate in exactly the same way as skin regions and so in an imageoverexposed for time T_(RO), such regions will tend to saturate and sothe entire image will wash out. Eye regions from the normally exposedversions of such images are recognized as belonging to a lifelesssubject (step 104) and, at this step enrollment may be interrupted.

On the other hand, if an eye region can be identified within theoverexposed image and an iris region can be identified within the eyeregion, step 102, as explained before in relation to step 105, then theprocess continues by generating and storing iris information for theextracted iris region(s), using the BAU 20, step 103.

So in summary, the enrollment stage comprises:

-   -   acquiring 100 a reference image of a predetermined subject        illuminated by an IR illumination source 16, wherein the        reference image is overexposed until its skin portion is        saturated;    -   identifying 102 at least one iris region 42 within the acquired        overexposed reference image; and    -   generating 103 and storing information for the identified iris        region 42.

Once enrollment is complete, the BAU 20 will ideally have irisinformation for an overexposed eye region of the subject and, ifdesired, iris information for a normally exposed eye region of thesubject. As indicated above, this reference information can be storedsecurely in memory 22, within secure memory in the BAU 20 or in anylocation accessible to the BAU 20.

It will be appreciated however that if ambient light conditions duringenrollment are not suitable, it may not be possible to acquire an imagewith an exposure time T_(RO) long enough to saturate the skin pixelswithout causing so much blur that an iris code cannot be extracted fromthe image. In this case, enrollment may be either permitted at leasttemporarily just based on the normally exposed reference image—or theuser may be required to delay enrollment until ambient light conditionsare suitable.

Referring now to FIG. 7, the authentication stage starts when a userwishes to either unlock the system or to unlock an application runningon the system.

The authentication stage starts by acquiring a probe image of the usernormally exposed for exposure time T_(N), step 198.

At step 199, a decision can be made as to whether or not the device isoperating in strong sunlight. One method to do so comprises analyzingthe areas around the eyes. If an image is captured indoors, the eyes areproperly illuminated and so it is assumed the image has been capturedindoors. If an image has been captured outdoors there are shadows 33around the eyes, such as the examples shown in FIGS. 2(a) and (b). Ifsuch shadows 33 are detected, apart from making eye and iris regionsdifficult to detect, it is assumed that the image has been acquiredoutdoors in strong sunlight. Another ready indicator which can be usedin addition or as an alternative is if T_(N) is extremely short forexample, less than 1/100 ms—if so this suggests that the device is beingoperated in a well-lit outdoor environment. Other indicators could alsobe used, such as checking the time of day—it is unlikely that imagesbeing acquired between 9 pm and 6 am are being acquired in strongsunlight, or checking device location. For example, GPS coordinates canindicate if a device is located outside in bright conditions or insideunder artificially lit conditions. Other indicators include testingwhether the device is connected to or detects a WiFi signal—if not thereis a good chance the device is outdoors. In any case, where the deviceis not located outside or in sufficiently well lit conditions, thenthere is a good chance that the increased time which might be requiredto saturate skin pixels in the image would be too long to avoid motionblur in an acquired image.

In this case, the process proceeds as normal by:

-   -   identifying 206 one or more iris regions within the        normally-exposed probe image acquired at step 198; and    -   analyzing 207 the identified iris regions to detect whether they        belong to the predetermined subject. In this case, if steps 105,        106 have been employed in the enrollment stage, then the iris        information from the normally exposed reference image for a        subject can be used to authenticate the iris region(s) of the        normally-exposed image acquired at step 198. If this        authentication is successful, the device or application is        unlocked and if not, the process might signal a failure to the        user and perhaps return to step 198 a maximum limited number of        times before permanently locking the device or application.

On the other hand, if at step 199, sunlight is detected, theauthentication method 200 proceeds with the execution of steps 201-205.Step 201 comprises:

-   -   deriving the skin map (step 201 a);    -   based on the derived skin map, determining a second exposure        time T_(O) greater than T_(N) (step 201 b); and    -   acquiring an overexposed probe image during T_(O) (step 201 c).

Again, T_(O) may be determined so as substantially all the skin pixelsof an imaged facial region will reach saturation level, as follows:T_(O)=T_(N)×K, where K is calculated as in step 100 b described above.

Again, the overexposed probe image acquired at step 201 c can be checkedto determine if it is suitable for iris recognition and if time T_(O)needs to be adjusted by repeating steps 201 a-201 c as described inrelation to FIG. 6.

Once a suitably overexposed probe image has been acquired, the processcontinues by detecting whether the overexposed image 36 acquired at step201 includes one or more eye regions 30, step 202.

If so, it means that the eye regions 30 did not saturate during theoverexposed acquisition at step 201, because they reflected the IR lightdifferently from the saturated skin portions 31 and this behavior isassociated with live eyes.

Hence, a positive determination at step 202 corresponds to a recognitionof liveness of the subject and the method 200 can proceed to steps 203and 204 to the authentication of the live subject as in steps 206, 207.However, in this case, the iris information from the overexposed probeimage acquired at step 100 c in enrollment can be used to authenticatethe iris region(s) of the overexposed image acquired at step 201 c.Preferably, the stored information for the reference iris regions storedat steps 103 and 106 during enrollment comprises an iris code, and steps204 and 207 comprise comparing an iris code generated from the one ormore iris regions identified at step 203 and 206 with the correspondingstored iris code.

A negative determination at step 202 corresponds instead to a lifelessrecognition of the subject and this causes authentication to stop andfor either the device and/or the application to be locked.

In summary, the authentication stage comprises:

-   -   acquiring 201 through the image sensor 14 a probe image        illuminated by an IR illumination source 16, wherein the probe        image comprises one or more eye regions and is overexposed until        its skin portions are saturated;    -   identifying one or more iris regions within the one or more eye        regions; and    -   analyzing the one or more identified iris regions to detect        whether they belong to the predetermined subject.

It will be appreciated that in sufficiently strong sunlight conditions,it might not be necessary to actively illuminate a subject using the IRillumination source 16 when acquiring either the reference or probeimages. In such conditions, either the enrollment or authenticationprocess could rely on the IR component of natural sunlight to expose thesubject as required. It might only be necessary to determine that theambient lighting levels were sufficiently high to provide a sufficientIR component for iris exposure to determine that the IR illuminationsource 16 did not need to be used. Nonetheless, if the eyes were shadedfrom sunlight, activating the IR illumination source 16 might still berequired even in strong sunlight. Thus, the method could be extended tofirst attempt to acquire an iris image exposed by natural sunlight aloneand, if the eyes were shaded in this image, subsequently acquiring aniris image exposed using the IR illumination source 16 (and ambientlight) as well as testing for liveness in each case.

It will be appreciated that the above described methods not only improveiris recognition in conditions of strong sunlight, but alsoadvantageously provide robust liveness recognition.

The invention claimed is:
 1. An image processing method for irisrecognition of a predetermined subject, comprising: a) acquiring throughan image sensor, a probe image illuminated by an infra-red (IR)illumination source, wherein said probe image comprises one or more eyeregions and is overexposed until skin portions of the image aresaturated; b) identifying one or more iris regions within said one ormore eye regions of said probe image; and c) analyzing the one or moreidentified iris regions to detect whether they belong to thepredetermined subject.
 2. The method according to claim 1, comprising:responsive to failing to identify one or more iris regions in said probeimage, designating said probe image as containing an image of a non-livesubject.
 3. The method according to claim 1, further comprisingenrolling the predetermined subject by: identifying a first iris regionwithin a reference image of the predetermined subject illuminated by anIR illumination source, wherein the reference image is overexposed untilskin portions of the reference image are saturated; and generating andstoring information for said identified first iris region.
 4. The methodaccording to claim 3 wherein said analyzing comprises: comparing saidstored information for the first iris region to information generatedfrom the one or more iris regions of said probe image to determine ifsaid probe image is of said predetermined subject.
 5. The methodaccording to claim 4, wherein: said stored information comprises a firstiris code; and said comparing comprises comparing an iris code generatedfrom the one or more iris regions identified within said probe image tosaid first iris code.
 6. The method according to claim 1, comprisingperforming steps a), b) and c) in response to ambient light exceeding athreshold.
 7. The method according to claim 3 further comprising: d)acquiring a normally-exposed probe image, said normally-exposed probeimage comprising one or more eye regions; e) identifying one or moreiris regions within said one or more eye regions of saidnormally-exposed probe image; and f) analyzing the one or moreidentified iris regions within said normally-exposed probe image todetect whether they belong to the predetermined subject.
 8. The methodaccording to claim 7, comprising performing steps d), e) and f) inresponse to ambient light not exceeding a threshold.
 9. The methodaccording to claim 7, wherein enrolling the predetermined subjectfurther comprises: identifying a second iris region from anormally-exposed reference image of the predetermined subjectilluminated by an IR illumination source; and generating and storinginformation for said identified second iris region.
 10. The methodaccording to claim 9 wherein said analyzing step f) comprises: comparingsaid stored information for the second iris region to informationgenerated from the one or more iris regions of said normally-exposedprobe image to determine if said normally-exposed probe image is of saidpredetermined subject.
 11. The method according to claim 7 comprisingdetermining an exposure time for said overexposed probe image as afunction of an exposure time for said normally-exposed probe image. 12.The method according to claim 11 wherein said determining an exposuretime comprises: identifying skin-pixels within said normally-exposedprobe image and determining said exposure time for said overexposedprobe image as a function of the intensity values of said skin-pixels.13. The method according to claim 9 comprising determining an exposuretime for said overexposed reference image as a function of an exposuretime for said normally-exposed reference image.
 14. The method accordingto claim 1 wherein the step of identifying one or more iris regions isperformed responsive to detecting one or more eye regions within saidprobe image.
 15. The method according to claim 1 wherein said IRillumination source comprises one or more of natural sunlight; or anartificial light source.
 16. The method according to claim 15 comprisingemploying said artificial light source in response to determining adegree of shading of eye regions detected in an image of the subjectilluminated with only ambient light.
 17. An image processing method foriris recognition of a predetermined subject, comprising: a) identifyinga first iris region from a first reference image of the predeterminedsubject illuminated by an IR illumination source, wherein said firstreference image is overexposed until skin portions of the referenceimage are saturated; b) identifying a second iris region from a secondreference image of the predetermined subject acquired under a normalexposure condition; and c) generating and storing information for saididentified first and second iris regions; in response to ambient lightexceeding a threshold: d) acquiring through an image sensor, an imageilluminated by an IR illumination source, wherein said image comprisesone or more eye regions and is overexposed until skin portions of theimage are saturated; e) identifying one or more iris regions within saidone or more eye regions; f) using said stored information for the firstiris region for comparing the one or more iris regions identified atstep e) to the first iris region; and g) detecting, based on thecomparison, whether the one or more iris regions identified at step e)belong to said predetermined subject; and in response to ambient lightnot exceeding a threshold: h) acquiring a normally exposed image, saidimage comprising one or more eye regions; i) identifying one or moreiris regions within said one or more eye regions; j) using said storedinformation for the second iris region for comparing the one or moreiris regions identified at step i) to the second iris region; and k)detecting, based on the comparison, whether the one or more iris regionsidentified at step i) belong to said predetermined subject.
 18. An imageprocessing system for iris recognition of a predetermined subject,comprising: an optical apparatus which comprises at least an imagesensor for acquiring an image and at least one IR illumination source;and image processing means arranged for executing the steps of claim 1.19. A computer program product comprising a non-transitory computerreadable storage medium on which instructions are stored which, whenexecuted on an image processing system, are configured for performingthe steps of claim 1.